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Enhanced iterative projection for subclass discriminant analysis under EM-alike framework

Tao, Yuting ; Yang, Jian ; Chang, Heyou

Pattern recognition, 2014-03, Vol.47 (3), p.1113-1125 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    Enhanced iterative projection for subclass discriminant analysis under EM-alike framework
  • Autor: Tao, Yuting ; Yang, Jian ; Chang, Heyou
  • Assuntos: Classification ; Discriminant analysis ; Division ; Eigenvalues ; EM-alike framework ; Generalized eigenvalue problem ; Iterative steps ; K-means clustering ; Larger sample size problems ; Linear discriminant analysis ; Mixture discriminant analysis ; Pattern recognition ; Subclass discriminant analysis
  • É parte de: Pattern recognition, 2014-03, Vol.47 (3), p.1113-1125
  • Notas: ObjectType-Article-2
    SourceType-Scholarly Journals-1
    ObjectType-Feature-1
    content type line 23
  • Descrição: Mixture discriminant analysis (MDA) and subclass discriminant analysis (SDA) belong to the supervised classification approaches. They have advantage over the standard linear discriminant analysis (LDA) in large sample size problems, since both of them divide the samples in each class into subclasses which keep locality but LDA does not. However, since the current MDA and SDA algorithms perform subclass division in just one step in the original data space before solving the generalized eigenvalue problem, two problems are exposed: (1) they ignore the relation among classes since subclass division is performed in each isolated class; (2) they cannot guarantee good performance of classifiers in the transformed space, because locality in the original data space may not be kept in the transformed space. To address these problems, this paper presents a new approach for subclass division based on k-means clustering in the projected space, class by class using the iterative steps under EM-alike framework. Experiments are performed on the artificial data set, the UCI machine learning data sets, the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, and the terrain cover database. Extensive experimental results demonstrate the performance advantages of the proposed method. •We focus on the classification of the large sample size problems.•We propose a new EM-alike iterative projection approach (EMIPA) for subclass division.•Our proposed approach outperforms the traditional MDA and SDA.•Our approach operates subclass division and eigenvector seeking class by class.•Our proposed approach takes only a bit more time than MDA and SDA.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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